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Multi-label learning aims to solve classification problems where instances are associated with a set of labels. In reality, it is generally easy to acquire unlabeled data but expensive or time-consuming to label them, and this situation becomes more serious in multi-label learning as an instance needs to be annotated with several labels. Hence, semi-supervised multi-label learning approaches emerge as they are able to exploit unlabeled data to help train predictive models. This work proposes a novel approach called Self-paced Multi-label Co-Training (SMCT). It leverages the well-known co-training paradigm to iteratively train two classifiers on two views of a dataset and communicate one classifier’s predictions on unlabeled data to augment the other’s training set. As pseudo labels may be false in iterative training, self-paced learning is integrated into SMCT to rectify false pseudo labels and avoid error accumulation …